Recent research has talked about “millennials” being less likely to get their driver’s license at younger ages, with data showing a decline over the 2000s. But is this trend continuing? This post checks out the latest data to see if the decline is still happening.

While I’m at it, I’ll look at license ownership by age and gender (are young men more likely to have a license than women?) and trends for older persons (are people holding onto licenses longer into old age?). There’s also a strange quirk for people born in 1945/6.

This post analyses available state-based data on driver’s license ownership in Australia in recent years. In this post some of the data sets I’ve used include learner’s permits, and some only count “independent” licenses (watch for notes).

How does license ownership vary with age?

I have access to licensing data for four Australian states that allows a quite detailed analysis (three publicly, and VicRoads kindly let me access theirs).

The following chart shows license ownership for Victoria, South Australia and New South Wales by individual age for the most recent year available at the time of writing (combining licensing data with ABS state population estimates by single year of age to calculate ownership rates):

License ownership peaks between ages in the mid-thirties to late sixties, then falls away with age thereafter. There is certainly a pattern of people in their 20s and early 30s being less likely to have their license.

The main difference for the younger ages is that the Victoria data include independent licenses only (ie excludes people with a learner’s permit). Also, from what I understand, the minimum age for an independent driver’s license is 17 in most states, except in Victoria where it is 18, and the Northern Territory where it appears to be theoretically possible at age 16 and 6 months. The minimum age for a learner’s permit is 16 years, except in the ACT where it is 15 years and 9 months.

The Victoria data is generally higher than the other states from around age 32, with some results calculated as high as 99.8%. The Victoria data includes suspended licenses, which may not be the case for other states, and there may be other minor differences in the way the data is counted. But it is interesting that Victorian ownership rates are up to 10% higher for older age groups. I’ll look at those trends and patterns in more detail shortly.

How does license ownership vary with gender (and age)?

First up, New South Wales:

Age 30 is the age under which females are more likely to have a license (or learner’s permit), and after which males are have higher rates of licensing. The difference in licensing between the genders grows very large for older ages. This might be explained by women of older generations being less likely to have ever obtained their license, and/or men stubbornly holding onto their license for longer than they should.

Here’s the same data for South Australia:

The gender flip occurs around age 27 – with younger women more likely to have their driver’s license.

Queensland data is available with slightly less age resolution:

The gender flip point occurs sometime between ages 21 and 24.

I really wasn’t expecting younger females to be more likely to have a license than males. Is this just something to do with learner’s permits?

I can only answer that question with Queensland data:

This shows women in Queensland are more likely to have their learner’s permit than men (at any age). However, men are actually more likely than women to have an independent license from age 20 onwards, as women appear to spend more time with their learner’s permit. It would be interesting to look at this for other states, but alas the data isn’t readily available.

You may also have noticed the South Australia and Queensland data suggests around 101% of men in some age groups have their driver’s license. This suggests imperfect data – perhaps double counting people with endorsements for higher classes of vehicle or people who have both car and motorbike licenses, or imperfect ABS estimates of people at each individual age. So licensing data needs to be read with caution, with a focus on the trends and patterns rather than exact numbers.

License ownership trends of younger people

There are clear and sizeable downward trends in license ownership rates amongst most ages, with most dropping by around 12% over 13 the years. There was a slight rise in most age groups in 2009 but then a quite significant fall between 2009 and 2010, particularly 18 and 19 year olds. The graduated licensing system was introduced between January 2007 and July 2008, and I’m yet to find references to changes in rules around 2009 or 2010. so I’m not sure how to explain the changes in 2010. That said, when I look at the data for 2010, there are a few anomalies in patterns in other age groups, so there may be some small data errors.

The rate of license ownership of 18 year olds was however relatively steady between 2001 and 2008, but then dropped significantly from 2010. The minimum time period to hold a learner’s permit became 12 months in July 2007, making it harder to obtain your probationary license by age 18. There is a peak of license ownership at age 18 in (June) 2009 – these people will have turned 16 in the financial year 2006-07 and so probably escaped the new licensing regime (I suspect this cohort made more effort to get their learner’s permit before 1 July 2007).

I also note that the declines appear to have largely levelled off for most ages since around 2011. I don’t have much data about learner’s permit holders in Victoria, but some data published shows that the average time spent on L plates in Victoria for people aged 17-20 increased from around 60-70 weeks in 2000 to around 100 weeks in 2010, following the graduated licensing scheme introduction.

Have we now stabilised at new lower levels? More on that shortly.

Readers of this blog will note that several transport trends changed direction in 2011. That was about the time that public transport patronage growth in Melbourne slowed down, and mode share stabilised. Here’s the Melbourne mass transit mode share and young persons licensing rates charted together:

That’s a strong correlation. Given that younger people dominate public transport patronage, this isn’t hugely surprising. The major deviation from the trend is 2009, which is perhaps explainable through changes to the licensing regime, although between 2001 and 2005 there was a reduction in license ownership without mode shift to mass transit.

So why has the trend towards lower license ownership of younger people stopped in Victoria? Other researchers might have to answer that question.

Here is data for young people in New South Wales (note: data includes learner’s permits):

Very different trends! Most age groups trended down between 2007 and 2010 but then many bounced up again thereafter.

So is this completely different trend because it includes learner’s permits? Unfortunately I don’t have single-age based data for people with independent licenses, but I do have it for age groups:

The trends are similar. Independent license ownership rates have dropped by around 3-5% in the younger age brackets over the nine years. There was a larger dip around 2009, followed by small rises in some age brackets since. Otherwise things look pretty stable, and very different to Victoria (and note that Sydney has had much less public transport patronage growth than Melbourne over the same time).

If I put learner’s and independent licences together and look at 20-24 year olds, there is a slightly higher proportion with their learner’s permit in more recent years (8% to 11%). So this suggests people are probably staying on their Ls for slightly longer.

What is interesting in the NSW data is that there appears to be a pattern that varies by birth year. I’ve adjusted the layout of the data tables such that each row represents people in a single birth year (well, birth financial year, if you will). This data includes learner’s permits.

License ownership rates were relatively higher for people born 1992 onwards (although curiously they appeared to have declined after age 21, which perhaps might be a result of immigration – not sure). In NSW, the birth years of approximately 1982 to 1991 appear to have had relatively lower rates of license ownership.

Only six years of data is available for South Australia, but there is a pattern of higher license ownership for people born from around 1993 onwards (data includes learner’s permits), but it might be trending downwards again from birth years 1996 onwards:

(note: the only available South Australia data for 2010 is for January – I have interpolated to estimate June 2010 numbers. The most recent data is for March 2014 – I have interpolated the population estimate accordingly but in the figure above the bottom numbers in each column are for people born in the 12 months to March 1998, not 12 months to June 1998).

Queensland data only reports single year license ownership to age 20, but a much longer time series is available:

Again, there is a range of birth years from around 1984 to 1990 with relatively lower license ownership. In July 2007 the minimum age for a learner’s permit dropped to 16, which would explain the massive increase in learner’s permit ownership for 16 year olds. This seems to correspond to increased licensing rates for birth years 1993 onwards.

Although Victoria hasn’t had a bounce in license ownership rates for young people, the birth year trends do show the downward trend finishing with births around 1990. In fact, from that birth year the rate of license ownership at age 21 went up slightly, which might reflect that it is easier to obtain a probationary license from that age (as a learner logbook is no longer required). The decline in licensing rates seemed to begin around birth year 1980.

The following table summaries the birth years of lower license ownership in each state, and compares these birth years with the first birth year impacted by graduated by graduated licensing (assuming people obtain their learner’s permit at age 16):

Lower license
ownership birth years

Graduated licensing started

Start

End

Start

First birth year

New South Wales

1982

1991

2000

1984

Victoria

1980

1990

2007

1991

South Australia

?

1993

2005

1989

Queensland

1985

1990

2007

1991

There appears to be a fairly consistent cohort of people born between around 1980-5 and 1990-3 who have been less likely to get their license at a younger age. In Victoria and Queensland they weren’t faced with the new graduated licensing system if they got their learner’s permit at age 16. In fact, licensing rates stopping declining in the birth years first fully subjected to graduated licensing in Queensland and Victoria – the opposite of what you might expect!

Dr Alexa Delbosc at Monash University has led much interesting research into reasons for the downwards trend of license ownership in Australia (amongst others). Perhaps this new evidence of a reversal/stabilisation of the trend might explain some things further, or need a new explanation in itself.

What about license ownership of older people?

First up, Victoria:

License ownership rates increased in Victoria until 2011 in most age groups, probably reflecting people living healthier for longer. However license ownership rates fell from 2011 onwards for those over 80 (despite Victoria not having mandatory testing for older drivers). It might be explained by a change to a 3 year license renewal period for those over 75, but I cannot confirm when that change was implemented. I’m also at a loss to explain the blips in the 2009 data for those 90+.

Here’s the same for New South Wales, where car drivers need to have an annual medical review from age 75, and have to pass a practical test to keep an unrestricted license from age 85. For those 75+, licensing rates are considerably lower than in Victoria.

While the Queensland data provides less resolution of age groups, it shows the trend of increasing license ownership over a longer period of time, although with a levelling out for those 60-74 from around 2013.

And for completeness, here is five year’s worth of data available for South Australia, not showing any dramatic trends:

Finally, you might have spotted a blip in the first chart of this post around age 68 (you were looking at it carefully, right?). Zooming in and changing the X axis to birth year we see an interesting anomaly:

Years on the X axis are actually financial years (ending June). People born in 1946/7 are 10% more likely to have their driver’s license than people born in 1945/6, and this is consistent across three states!

This year of relatively lower license ownership is for people born immediately after World War II, and who around 19 years of age when Australia sent troops to the Vietnam War (although many men in the next birth year would also have gone to Vietnam). I wondered if it might be related to the Vietnam War, but then the trend applies more so to women than men, as shown in this NSW data:

I then thought it might be to do with being born whilst Australia was recovering from the war and healthy food and good medical care might have been less available, resulting in a mini-generation of people less likely to be able to get their driver’s licenses later in life.

The census provides one indicator of disability in terms of people recorder as “need assistance with core activities”. While people born between 1945 and 1949 seem to be slightly more likely to have a disability (compared to the general pattern across ages, supporting the post-war lower health hypothesis), the 1946 age year doesn’t stand out as much different to neighbouring years.

Another explanation might be that ABS have inaccurately estimated the population born in that year.

Who is cycling to work? Where do they live? Where do they work? How old are they? What work do they do? Do men commute by bicycle more than women? How far are cyclists commuting? What other modes are cyclists using?

The census provides some answer to these questions for the entire Australian working population, albeit for one winter’s day every five years.

This post builds on material I presented at the Bike Futures 2013 conference, using census data from across Australian with a little more detail on capital cities and my home city Melbourne.

It’s not a short post, so settle in for 13 charts and 17 maps of data analysis.

How has cycling mode share changed over time?

The first chart shows the proportion of journeys to work by bicycle (only) in Australia’s capital cities.

Darwin appears to the capital of cycling to work, although it is quickly losing ground to Canberra (unfortunately I don’t have figures for Darwin pre-1996). The census is conducted in Darwin’s dry season, but other data suggests there is little difference in bicycle activity between the wet and dry seasons.

Melbourne has shown very strong growth since 2001 and Sydney showed strong growth between 2006 and 2011. Cycling mode share has grown in all cities since 1996.

Mode shares collapsed in Adelaide, Sydney, Brisbane, and Melbourne between 1991 and 1996, which many people have attributed to the introduction of mandatory helmet laws (Alan Davies has a good discussion about this issue on his blog).

But as I pointed out at the start, census data is only good for one winter’s day every five years. Does the weather on these days impact the results?

Here is a chart roughly summarising the weather in (most of) the capital cities for 2001, 2006 and 2011 in terms of minimum temperature, maximum temperature and rainfall. It doesn’t cover wind, nor what time of day it rained (although perhaps some fair-weather cyclists might avoid riding on any forecast rain). It also fails to show the sub-zero minimums in Canberra (involves asking too much from Excel).

You can see that 2011 was wetter in Adelaide and Hobart than previous years, and this coincides with lower cycling mode shares in these cities in 2011. So census data is quite problematic from a weather point of view. That said, most cities had very little or no rain on the last three census days.

Where were the commuter cyclists living and working?

Other posts on this blog have also covered some of these maps, but not for all cities.

Some of the following maps are animated to show both 2006 and 2011 results, and note that the colour scales are not the same for all maps. I’ve sometimes zoomed into inner city areas when these are the only places with significant cycling mode share. White sections on maps represent areas with low density, or where the number of overall commuters was very small (sorry I haven’t gone to the effort of making every map 100% consistent, but rest assured the areas in white are less interesting). Click on the maps to see more detail.

Canberra

Firstly home locations:

The cycling commuters mostly appear to be coming from the inner northern suburbs. I don’t know Canberra intimately, but Google maps doesn’t show a higher concentration of cycling infrastructure in this area compared to the rest of Canberra.

The highest mode share was 12% in the SA2 of Acton, which is dominated by the Australian National University. Perhaps a lot of the university staff live in the inner northern suburbs of Canberra?

Melbourne

By home location:

Cycling mode share is highest for origins in the inner northern suburbs and has grown strongly since 2006. There’s also been some growth in the Maribyrnong and Port Phillip council areas off a lower base.

By work location (note: this data is at the smaller destination zone geography):

Cycling to work boomed in inner Melbourne between 2006 and 2011, particularly to workplaces in the inner north. Princess Hill had the highest bike share of 14% in 2011 (possibly dominated by Princess Hill Secondary College employees), followed by a pocket of south-west Carlton that jumped from around 5% to 13%. Apart from the inner north, there were notable increases in Richmond, Balaclava, Yarraville and Southbank. Cycling rates within the CBD are relatively low, perhaps reflecting limited cycling infrastructure on CBD most streets in 2006 and 2011.

Adelaide

Firstly, by home:

Adelaide appears to lack any major concentrations of cycling, although slightly higher levels are found just outside the parkland surrounding the CBD.

Secondly, bicycle mode share by work destination at the (larger) SA2 geography:

The numbers are all small, with 3% in the (large) Adelaide CBD. I imagine a map based on destination zones might show some pockets with higher mode share, but that data isn’t freely available unfortunately.

Perth

By home location:

The inner northern and western suburbs, and south of Fremantle seem to be the main areas of cycling growth.

For workplaces at the larger SA2 geography:

The highest mode share was in ‘Swanbourne – Mount Claremont’, only slightly ahead of ‘Nedlands – Dalkeith – Crawley’ – which contains the University of Western Australia. The Fremantle SA2 (with 3% bicycle mode share by destination) includes of Rottnest Island where around 20% of the 73 resident commuters cycled to work, but the result will be easily dominated by the mainland Fremantle section.

Again, I suspect some smaller pockets would have had higher mode shares if I had access to destination zone data.

Brisbane

By home location:

There was significant growth in cycling from the West End, and around the University of Queensland/St Lucia – which may be related to the opening of the Eleanor Schonell Bridge (after the 2006 census) which only carries pedestrians, cyclists and buses.

By work location (at larger SA2 geography):

The highest share was in St Lucia – which is probably dominated by the University of Queensland. Neighbouring Fairfield – Dutton Park came in second. These two areas are directly joined by the Eleanor Schonell Bridge which provides cycling a major advantage over private transport. It seems to have been quite successful at promoting cycling in these areas.

Sydney

First by home location:

There were quite noticeable shifts to cycling in the inner south and around Manly.

By work location (by smaller destination zone geography):

There was strong growth, again in the inner southern suburbs. In 2011 bicycle mode share was highest in Everleigh (11.5%) following by the University of NSW (Paddington) at 7.9% (excluding travel zones with less than 200 employees who travelled).

Rural Australia

Here’s a map showing bicycle share by SA2 workplace location for all of Australia, which gives a sense of bicycle mode shares in rural areas.

The SA2s in Australia with the highest cycling mode shares in 2011 (by home location) were:

Lord Howe Island, NSW: 21%

Acton, ACT (covering Australian National University): 12%

Port Douglas, Queensland: 10%

Parkville, Victoria (covering the University of Melbourne main campus): 8%

East Side, Northern Territory (Alice Springs): 8%

St Lucia, Queensland (covering the University of Queensland): 8%

How far did people cycle to work? (in Melbourne)

It is difficult to get precise distances for journeys to work, but approximations are possible. I’ve calculated the approximate distance for each journey to work by measuring the straight line distance between the centroid of the home and work SA2s and then rounded to the nearest whole km. To give a feel for how this looks, here is a map showing inner Melbourne SA2s and the approximate distances between selected SA2s:

This distance measure generally works well in inner city areas. However in the outer suburbs SA2s are often much larger in size, and sometimes only partially urbanised. However as we’ve seen above the volumes of cycling journeys to work are very low in these places, so that hopefully won’t skew the results signficantly.

Two-thirds of cycling journeys to work in Melbourne were approximately 5km or less, with 80% less than 7 km, and 30% were 2 km or less.

The longest commute recorded within Greater Melbourne was approximately 44km.

Was cycling combined with other modes?

The following chart shows that bicycles were seldom combined with other modes:

Around 16-17% of cycling commuters in the four largest cities in 2011 involved another mode. Use of other modes with cycling grew in all cities between 2006 and 2011

The next chart shows what these other modes were:

Sydney, Melbourne, Brisbane and Perth had high rates of bicycle use with trains, while combining car and bicycle was more common in the smaller cities.

The next chart shows the number of trips involving bicycle and trains in 2006 and 2011:

The chart shows the relative success of Melbourne Parkiteer program of introducing high quality bicycle cages at train stations, which has helped boost the number of people access the train network by bicycle by around 600 between 2006 and 2011. I understand a similar project has been undertaken in Perth which saw growth of around 250.

In Melbourne, the home locations for people using bicycle and train are extremely scattered – the following map shows a seemingly random smattering:

How does commuter cycling vary by age and sex?

This chart shows remarkably clear patterns. Males were much more likely to cycle to work. Teenage boys (particularly those under driving age) had the highest cycling mode shares (with teenage girls much less likely to cycle). The next peak for men was around the mid thirties, and women’s mode share peaked around ages 28-32.

Where are women more likely to cycle to work?

Women are sometimes talked about as the “indicator species” for cycling – ie if you have large numbers of women cycling compared to men then maybe you have good cycling infrastructure that attracts a broader range of people.

The census data can shed some light on this. For each SA2 in Melbourne, I have calculated the male and female cycling mode shares both as a home origin, and as a work destination (this analysis looks at people who only used bicycle (and walking) in their journey to work). I’ve then calculated the ratio of male mode share to female mode for each area (SA2).

I’ve used the ratio of mode shares in preference to the straight gender split of cycling commuters – as female workforce participation is generally lower and there can be spatial variations in the gender split of the workforce. 46% of all journeys within Greater Melbourne in the 2011 census were by females, but only 28% of cycling journeys to work were by females.

The following map shows the ratio of male to female cycling mode shares by home location for SA2s (with more than 50 commuter cyclists, and where the bicycle mode share is above 1%):

Areas attracting comparable female and male bicycle shares include the inner northern suburbs and – curiously – Toorak (probably many using the off-road Gardiners Creek and Yarra Trails to access the city centre).

Here’s a similar map, but by workplace areas:

The patterns are much more pronounced. Six SA2s had higher female mode shares than male: Yarraville, Fitzroy North, Brunswick East, Ascot Vale, Carlton North – Princes Hill, and Elsternwick.

The areas with near-1 ratios of male to female mode shares were similar to the areas with higher total cycling mode shares. The following chart confirms this relationship (note areas with cycling mode shares below 1% not shown):

What this also shows is that home-area mode shares reach much higher values than workplace-area mode shares. Perhaps the secret is in the home-area cycling infrastructure? Or perhaps it’s more to do with the residential demographics?

See the Bicycle Network Victoria website for more data about female cycling rates in Melbourne.

Do women cycle the same distances as men?

Again using the approximate straight line commuting distances (as explained above) the following chart shows that women’s cycling commutes are a little shorter than men’s, but not by much:

The median female cycling commute was approximately 1.8 km shorter than for males.

What types of workers are more likely to cycle to work?

Firstly, I’ve looked at the differences between public and private sector employees.

Before I dive into the data, it’s important to recognise that different types of workers are not evenly spread across Australia. Some types of jobs concentrate in city centres while others might be more likely to be found in the suburbs or the country. Therefore many of the following charts show results for Australia as a whole, but also for people working in central Melbourne (the SA2s of Melbourne, Carlton, Docklands, East Melbourne, North Melbourne and Southbank), which has a relatively high rate of cycling to work.

The data suggests public servants were much more likely to cycle to work:

The local government result has prompted me to calculate the cycling mode shares for local government workers across Australia (assuming workers work within the council for which they work). Here are bicycle mode shares for the top 20 councils for employee cycling mode share in the census:

Council

State

Bicycle mode share

Tumby Bay (DC)

SA

23.5%

Kent (S)

WA

18.8%

Carnamah (S)

WA

16.0%

Central Highlands (M)

Qld

14.3%

Uralla (A)

NSW

13.8%

Wakefield (DC)

SA

13.5%

Nannup (S)

WA

12.5%

Broome (S)

WA

12.1%

Alice Springs (T)

NT

11.8%

Narembeen (S)

WA

11.5%

Blackall Tambo (R)

Qld

11.3%

Kowanyama (S)

Qld

11.2%

Exmouth (S)

WA

11.1%

Yarra (C)

Vic

10.4%

Glamorgan/Spring Bay (M)

Tas

8.7%

Torres (S)

Tas

8.6%

Yarriambiack (S)

Qld

8.3%

Mallala (DC)

Vic

8.0%

Richmond Valley (A)

NSW

7.2%

McKinlay (S)

Qld

6.7%

Most of the top 20 are non-metropolitan councils. Melbourne’s City of Yarra is the top metropolitan city council (within Greater Melbourne the next highest councils are Moreland 6.1%, Port Phillip 5.6%, Melbourne 5.6% and then Stonnington 4.9%).

National government employees had the highest bicycle mode share of all of Australia. I suspect this relates to university staff, as many of the earlier maps showed university campuses often had relatively high rates of employees cycling (85% of “higher education” employees count as “national government” employees).

The census data can also be disaggregated by income:

Cycling mode shares were highest for people on high incomes. Initially I thought this might reflect the fact that high income jobs are often in city centres were cycling is relatively competitive with private and public transport. However, even within central Melbourne workers, cycling rates are higher for those on high incomes (curiously with a second peak for those on incomes between $300 and $399 per week).

Does cycling to work make you healthier and therefore more likely to get promoted and earn a higher income? Or are employers offering workplace cycling facilities to attract highly paid staff? I haven’t got data that answer those questions.

Consistent with higher rates of cycling for higher income earners, those in more highly skilled occupations were more likely to cycle to work:

I suspect this might reflect the presence/absence of workplace cycling facilities (perhaps office workplaces are more likely to provide cycling facilities than retailers for example) and/or the ability to afford to live close to work (which makes cycling easier).

Are recent immigrants more likely to ride to work?

This one really surprised me and I only investigated it because it was possible to do. The census asks what year people migrated to Australia (if not born here), and it turns out that recent immigrants were much more likely to cycle to work:

This might be explained by the demographics of recent immigrants (eg car ownership, home location, income levels, occupation and age).

I’d welcome comments on any other trends people might spot in the data.

While Australian cities have been growing outwards with new suburbia, they have also been getting denser in established areas, and the new areas on the fringe are often more dense than growth areas used to be (see last post). So what’s the net effect – are Australian cities getting more or less dense?

This post also explores some of the issues in calculating population-weighted density.

Measuring density

Under the traditional measure of density, you’d simply divide the population of a city by the total metropolitan area’s area (in hectares). As the boundary of the metropolitan area seldom changes, the average density would simply increase in line with population with this measure. But that density value would also be way below the density at which the average resident lives, and so not very meaningful.

Enter population-weighted density (which I’ve looked at previously here and here). Population-weighted density takes a weighted average of the density of all parcels of land that make up a city, with each parcel weighted by its population. One way to think about it is the residential density in which the “average resident” lives.

So the large low-density parcels of rural land outside the urbanised area but inside the “metropolitan area” count very little in the weighted average because of their small population relative to the urbanised areas. This means population-weighted density pretty much overcomes having to worry about the boundaries of the “urban area” of a city. Indeed, in a previous post I found that removing low density parcels of land had very little impact on calculations of population-weighted density for Australian cities. (However, the size of the parcels of land used in a population-weighted density calculation will have an impact, as we will see shortly).

Calculations of population-weighted density can answer the question about whether the “average density” of a city has been increasing or decreasing.

Population-weighted density of Australian cities over time

Firstly, here is a look at population-weighted density of the six largest Australian cities, measured at SA2 level (the smallest geography for which there exists a good consistent set of time-series estimates). I’ve made this chart tall so you can see the trends in the less dense cities.

According to this data, most cities bottomed out in density in the mid 1990s, but SA2 population data is only available back to 1991.

We can calculate population-weighted density back to 1981 using the larger SA3 geography (an SA3 is roughly similar to a local government area (in Melbourne at least), so getting quite large).

This shows that most cities were getting less dense in the 1980s (Melbourne quite dramatically), with the notable exception of Perth. I expect these trends could be related to changes in housing/planning policy over time.

When measured at SA2 level, the four smaller cities had almost the same density in 2011, but at SA3 level, there is more separating them. My guess is that the arbitrary nature of geographic boundaries is having an impact here. I would also guess that using smaller geographic units would produce “cleaner” results as larger tracts of non-urban land are more likely to be split from residential land.

I’ve not included the Australian Capital Territory (Canberra) on this chart because it only has nine SA3s, and the shape of its curve does not resemble the SA2 trend at all.

Melbourne’s population-weighted density over time

I’ve taken a more detailed look at my home town Melbourne, using all available ABS population figures for the geographic units ranging from mesh blocks to SA3s inside “Greater Melbourne” (as defined in 2011), to produce the following chart:

The data suggests 1994 was the turning point in Melbourne where the population-weighted density started increasing (not that 1994 was a particularly momentous year – the population-weighted density increased by a whopping 0.0559 persons per hectare in the year to June 1995 (measured at SA2 level)).

You’ll also note that the density values are very different when measured on different geographic units. That’s because larger units include more of a mix of residential and non-residential land. The highest density values are calculated using mesh blocks (MB), which often separate out even small pockets of non-residential land (eg local parks) from residential land. Indeed 25% of mesh blocks in Australia had zero population, while only 2% of SA1s had zero population (at the 2011 census). At the other end of the scale, SA3s are roughly the size of local councils and include parklands, employment land, rural land, airports, freeways, etc which dilutes their average density.

In the case of SA2 and SA3 units, the same geographic areas have been used in the data for all years. On the other hand, Census Collector Districts (CD) often changed between each five-yearly census, but I am assuming the guidelines for their creation would not have changed significantly.

Implications for international comparisons of population-weighted density

To be able to meaningfully compare population-weighted densities internationally, two statistical agencies would have to have a unit geography level created with the same guidelines in terms of area and average population, which I’m guessing is quite unlikely (Australia’s new statistical geography is described here).

For reference:

90% of Australian mesh blocks have a population of less than 130 people.

90% of Australian SA1s have a population of less than 568 (but only 10% have less than 223). The guideline is 200-800 residents, with an average of 400 (the actual average was 392 in 2011).

90% of Australian SA2s have a population of less than 20,000 and 10% have less than 3,000, making them highly variable and difficult to compare with other cities.

US census tracts have a target population of 2,500 to 8,000 residents (averaging 4,000).

US census blocks have a target population of around 400 housing units (ranging 250 to 550), which is probably a population of roughly 500 to 1500 persons, quite a bit larger than an Australian SA1.

Canadian dissemination areas have a target population of 400 to 700 persons, slightly larger than an Australian SA1.

Census output areas in England and Wales have a target population of between 100 and 625 persons, a little smaller than Australian SA1s.

I’ll stop there, but it doesn’t look good for finding compatible geography levels.

Maybe there is some statistically sound way of adjusting values for different sizes of geographic units(?). Or maybe we’ll just never really know which cities are truly denser than others.

I would therefore suggest great caution is applied in comparing the densities of cities internationally (and therefore inferring trends on the relationship between density and transport patterns using a mix of cities from different countries).

Following on from my recent post about the changing socio-economic landscape of Melbourne, this post simply looks at the changing shape and density of urban Melbourne using 5-yearly census data at collector district (1986-2006) and SA1 level (2011).

Straight to it: here is map of Melbourne residential density, click to enlarge and animate:

You can see the sprawl of Melbourne over the years, including changes that suggest shifts in the urban growth boundary after development previously seemed to have stopped against a line (particularly evident on the western edge of the City of Brimbank).

Here is another animated map showing the inner city area, with a density scale ranging from 10 to 100 persons/ha, so you can distinguish higher densities than the map above. Click to enlarge and animate.

You can see a lot more going on in established areas on this map, including densification in the CBD, St Kilda, St Kilda Road (conversion from office space), Parkville, Port Melbourne around Bay Street, Kensington Banks, Brunswick, Fitzroy, Southbank, South Melbourne, Elwood, Maribyrnong, Carlton, and many more.

A few things to note:

The size of the districts changes each year, particularly around the fringe. You’ll often see a large red patch where a larger block is only partly inhabited in one year, only to be replaced by smaller denser patches in future years. Patches of green that disappear might be the enlargement of a district causing a blending out of a small pocket of high density, rather than an actual drop in density.

Shades of pink indicate densities between 5 and 10 per hectare on the large map, and between 10 and 20 per hectare on the inner map. Lower densities are shown as white.

In 2011 the ABS changed their statistical geography. I have used SA1s from 2011 as the most comparable area unit to a census collector district, however they are generally smaller and so densities may appear to jump slightly in 2011 in some areas.

This post is drifting a little away from transport, but I hope you will find this interesting…

How has the spatial distribution of socio-economic advantage and disadvantage changed over time in Melbourne? (oh, and Geelong too)

The animated maps below are fascinating, but of course there’s lots of important caveats regarding the data.

About the data

Since 1986, the Australian Bureau of Statistics (ABS) has calculated Socio-Economic Indexes For Areas (SEIFA) based on five-yearly census data. These include indexes of relative socio-economic disadvantage (IRSD), and – since 2001 – an index of relative socio-economic advantage and disadvantage (IRSAD). For 2006 and 2011, SEIFA was explicitly designed to measure “people’s access to material and social resources, and their ability to participate in society” (with similar intent for prior years).

This post looks at the spatial changes over time in these index values. I must be upfront: ABS explicitly cautions this type of analysis. This is mostly because the component census variables that make up SEIFA scores and their respective weightings vary between each census, but also because statistical area boundaries change, the number of areas has increased, and indexes were calculated on usual residents from 2006 onwards (as opposed to people present on census night for 2001 and earlier). ABS also notes that middle range scores are very similar, so time-series analysis should focus more on the top and bottom ends of the spectrum. More discussion on this issue is available from ABS and .id consulting.

However, I’m going ahead noting the above (as readers also should!), on the following basis:

The intent of the indexes has not changed over time, although the quality has (perhaps one day ABS will recalculate SEIFA values for previous census using better measures where possible)

I’ve used percentile ranks within Victoria to get around the issue of the changing meaning of particular index values (although this might cause some issues if there has been a relative difference in changes between Melbourne and regional Victoria)

I’ve included a summary of the component variables that have changed between censuses (documentation is available from 1996 onwards)

I’m mapping this at a metropolitan scale with a view to looking at regional variations, rather than very local changes. In the following maps you’ll see fairly strong regional patterns

My analysis will focus only on substantial shifts (which have indeed occurred)

Excessive caution may mean that we never do any interesting analysis!

Changes in Index of Relative Socio-economic Disadvantage (IRSD)

This index has been available from 1986 onwards.

More significant changes in the make up of this index in recent years include:

2006 dropped Elementary Clerical, Sales and Service workers and tradepersons

2006 changed the evaluation of household income to consider ‘equivalised household income’ replacing a number of measures that try to capture income levels relating different household make-up scenarios. It also stopped using gender specific measures of people with certain occupations or unemployed

2001 saw no changes to the included variables from 1996

Variables for persons who did or didn’t finish year 12 at school have changed slightly in both 2006 and 2011

Click on this map to enlarge and see an animation of IRSD percentile values for the years 1986 to 2011.

You can see some quite dramatic changes over time. Two big trends of note are:

Most inner city suburbs have gone from being some of the most disadvantaged to much less disadvantaged. It’s hard to imagine suburbs such as South Yarra and East Melbourne as being highly disadvantaged, but the data suggests that was the case in the 1980s. During this transformation, pockets of high disadvantage have remained, probably reflecting older government housing estates. There appears to have a been a fairly large change between 1986 and 1991. This could represent dramatic demographic change and/or reflect changes in the calculations of SEIFA index values.

Areas with the highest disadvantage have generally shifted away from the city centre (including some middle suburbs such as Carnegie), perhaps reflecting the growth in high-end CBD jobs driving the attractiveness of near city living.

New urban fringe growth areas often begin with low levels of disadvantage, but have become more disadvantaged over time. This is particularly evident in areas such as Hoppers Crossing, Werribee, Melton, Deer Park, Craigieburn, Keysborough, Karingal, Epping, Hampton Park, Cranbourne, Altona Meadows and Keilor Downs. Perhaps this is because when these areas were initially settled there were many double-income-no-kids households that now have more kids and less income? It could also be a reflection of a turnover in the resident population.

The maps only show geographic units with a population density of 5 per hectare or more, so you can also see the urban growth of Melbourne (more on that in a upcoming post).

Changes in Index of Relative Socio-economic Advantage and Disadvantage (IRSAD)

This index was first calculated in 2001 and aims to also measure advantage, not just factors that suggest disadvantage. In 2011 it included all but one of the IRSD variables, plus a number that describe levels of advantage (eg high income, higher education, occupations such as managers and professionals, high rent or mortgage payments, spare bedrooms).

The component variables of IRSAD have changed in line with the changes to IRSD, plus some other variables:

2011 added people with occupation classed as managers, houses with spare bedrooms, households with 3 or more cars

2006 added people paying low/high rent, high mortgage payments, renting from government authority, households with no car, households with broadband internet connection (replacing persons using the internet at home)

A north-south divide through Heidelberg Heights, roughly parallel to the Hurstbridge rail line

Along the Dingley Arterial between Dingley Village and Springvale

How different are IRSAD and IRSD values?

IRSAD contains a lot more variables and uses different weightings. See the ABS website for full details.

For those who are interested in the correlation between the two, here’s a scatter plot for both 2006 and 2011 data comparing the two index values (as percentile ranks) for all CDs and SA1s (respectively) in Victoria:

You can see the relationships between the two indexes is stronger in 2011 (R-squared = 0.96) versus 2006 (R-squared = 0.89). This might reflect the make up of the variables in each year and/or the smaller geographic units in 2011 (SA1s) which may reduce diversity within each geographic unit.

I’m sure others could spot other interesting patterns, and/or offer explanations for the changes over time (comments welcome).

Since my first post looking at 2011 Melbourne residential density, there’s been a heap of new 2011 census data released. This post includes new maps showing Melbourne’s population density in maximum detail, as well as some more calculations of Melbourne’s urban/residential density for the density nerds.

Melbourne’s residential density in extremely high resolution

2011 population figures are now available for mesh blocks – the smallest ABS geographic unit. This allows a fine-grained look at 2011 residential density, and comparisons with 2006 as we now have a time series.

Here’s a very large animated map (4.7MB, 6825 x 4799 pixels) showing residential density at mesh block level for 2006 and 2011. You’ll need to click on it to download and see the animation (I’d suggest a new tab or window). Use your browser to zoom in and scroll around to areas of interest.

You can see that new growth areas on the fringe actually have relatively high densities, contrary to conventional wisdom. I also note a relatively high and increasing density in the Springvale/Keysborough/Noble Park area, quite some distance from the CBD. If you look carefully you will also spot infill developments like Waverley Park, Parkville (ex-Commonwealth Games village), Gresswell Hill in Macleod, Docklands, Maidstone, Edgewater estate in Maribyrnong, along St Kilda Road, Waterways, and no doubt many more.

More values for the urban/residential density of Melbourne

Okay, you might want to stop reading here unless you have a deep interest in density calculation methodology.

Along with mesh blocks, the recently released census data provides boundaries for urban centres and localities, which each representing a relatively continuous urban area (including residential and non-residential land). There is an urban centre of “Melbourne” defined, which excludes the satellite urban centres of Pakenham, Melton, Sunbury, Healesville and towns along the Warburton Highway, but includes the major urban regions along the Mornington Peninsula to Portsea and Hastings.

All this new data enables calculation of yet more values of the urban/residential density of Melbourne, adding to my previous list (some of which I have repeated for comparison purposes). The areas covered by each calculation are shown on the map below.

I note that the Melbourne urban centre is approximately a quarter of the area of “Greater Melbourne”.

Here’s a reference map of Melbourne showing the Greater Capital City Statistical Area, Statistical Division and Urban Centre boundaries of “Melbourne”, together with mesh blocks of above 1 and 5 persons/ha.

Finally, for the density nerds who are still reading this post, I have calculated the 2011 population-weighted density of Greater Melbourne using mesh blocks to be 42.8 persons/ha, which is much higher than the population-weighted density using SA1 geography of 31.8 persons/ha. It’s higher because more non-residential land parcels have been excluded from the overall calculation. If I restrict myself to mesh blocks within the Melbourne urban centre, the population-weighted density is only slightly higher at 45.1 persons/ha.

So if you want to compare population-weighted densities of different cities, you’ll need to make sure you are using equivalent geographic units, which I suspect would be very difficult for international comparisons. An attempt at Australian and Canadian city comparisons was made in the comments section of a previous post.

There you go. Next time someone claims to know the urban density of Melbourne, you can now argue with them for hours about whether you agree with their number and how it should be measured.